1. Field of the Invention
The present invention generally relates to a method for determining a frontal face pose, and more particularly, to a method for determining a frontal face pose by using the symmetry of a face.
2. Description of the Related Art
Generally, the physical quantification of a pose of a face requires knowledge of rotation information of three orientation axes—yaw, pitch, and roll. However, since image information which is input through a camera is projected onto a two-dimensional (2D) plane, the measurement of the axes is impossible in practice. As a result, research has been conducted on a scheme in which a shape of a face is modeled in a three-dimensional (3D) form like a cylinder, a relationship with an image-projected plane is calculated, and then a pose of a face is estimated. However, this scheme has to repetitively calculate a relational inverse matrix between a model and a projected plane, requiring a large amount of processing time and high complexity. Consequently, this scheme cannot be applied to a real-time application in an embedded environment since the performance of such a system has a tolerance error of 10 degrees with respect to a measured value.
Much research has been conducted on a method for detecting or tracking several feature points (e.g., eyes, a nose, a mouth, etc.) in a detected face and estimating a pose of the face by using geometric relationships (e.g., a distance, an angle, an area, etc.) among them (“the feature point tracking-based approach”). This approach is more advantageous than the cylinder model-based approach in terms of speed, but the geometric pose estimation method is meaningful only when each feature point has to be independently detected and the accuracy of the detected position has to be guaranteed, i.e., corrected, whereby the amount of computation is not small and the performance of the method may not be superior over the cylinder model-based approach.
The above-mentioned methods are used to estimate a pose of a face (i.e., yaw, pitch, or roll), and a method generally used for solving a problem of determining whether the pose is a frontal face pose is achieved mostly by learning. In other words, a possible range of the frontal face pose is defined, learning data corresponding to the frontal face pose and learning data corresponding to non-frontal face pose are collected in large amounts, and then a classifier which is capable of distinguishing the learning data to the maximum is designed through a learning method. To this end, pattern recognition methods such as a neural network or a Support Vector Machine (SVM) are often used.
However, conventional methods for analyzing a pose of a face require significant computation time. The model-based approach is difficult to apply to a single image and even for moving images, errors must be corrected regularly. For this reason, the model-based approach is not suitable for an embedded system requiring a real-time pose analyzing feature. The feature point tracking-based approach needs a basis of accurate detection of necessary feature points, and has a high error rate and difficulty in generating results in real time.
Conventional methods for analyzing a face by using its symmetry have been used to detect a region of a face in an image, but these methods have many problems in practice since they have restrictions on the face pose due to the symmetry of the face.